A Web Service for Fragment-based Selectivity Analysis of Drug Leads

基于片段的先导药物选择性分析的 Web 服务

基本信息

批准号：
9906478
负责人：
John Laurence Kulp III
金额：
$ 23.47万
依托单位：
CONIFER POINT PHARMACEUTICALS, LLC
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-02-01 至 2022-09-09
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9906478
关键词：
Address Adopted Affinity Algorithms Amino Acids Area Benchmarking Binding Binding Sites Chemicals Clinical Clinical Trials Cloud Service DHFR gene Data Data Set Drug Design Failure Family member Free Energy G-Protein-Coupled Receptors Goals Grant Hand Industry Internet Isoenzymes JAK2 gene JAK3 gene Journals Location Maps Measures Medicine Methodology Mutation Online Systems Outcome PTGS1 gene PTGS2 gene Patients Pattern Peer Review Peptide Hydrolases Pharmaceutical Preparations Phase Phosphotransferases Property Protein Family Protein Fragment Protein Isoforms Proteins Publications Publishing Research Personnel Resources Services Sirtuins Small Business Innovation Research Grant Software Tools Specificity Speed Technology Testing Therapeutic Toxic effect United States National Institutes of Health base clinical candidate design drug discovery enzyme pathway improved innovation lead optimization novel programs prototype repository side effect small molecule success tool web based interface web page web services web site

项目摘要

Abstract Significance: The goal of this proposal is to provide drug researchers with compelling new tools that address off-target selectivity in the rational design of drugs. To do this, we propose to employ our large repository of maps of where chemical fragments tightly bind to 1,000’s of proteins (Boltzmann maps) in searches for differ- ences in spatial binding patterns of fragments across protein families. This will empower medicinal chemists in attacking the off-target toxicity challenge that plagues clinical candidates in drug discovery. Our team is currently supported by an NIH Phase II SBIR grant (2R44GM109549) to computationally produce BMaps on a large scale (1,000’s of fragment maps on 1,000’s of proteins) for fragment-based drug design (see www.boltzmannmaps.com). Building upon this, the need is to build fast tools for the design of selective com- pounds that search these fragment maps in comparing binding patterns across a large number of proteins. Putting this capability in the hands of medicinal chemists across the industry via the Web has the potential to significantly improve those clinical outcomes negatively impacted by off-target toxicities and speed the delivery of new medicines to patients. Innovation: Comparing chemical fragment binding across a large number of diverse isozymes within protein families, based on our unique repository of fragment binding maps, is a new scientific approach to the rational design of selectivity. To efficiently query our large repository for binding patterns requires that we devise a new geometric search algorithm. Aim 1: Develop a novel algorithm for comparing fragment binding patterns using geometric hashing, supple- mented with other parameters such as the relative free energy from the chemical potential ranking and other physiochemical properties of the local binding site. We will provide a Web-based interface for requesting and visualizing search results. Aim 2: Validate the tools developed under Aim 1 in a proof-of-concept project to assess selectivity of a proprie- tary set of 127 compounds across the sirtuin family of proteins confirming experimental data. Overall Impact: In summary, having a body of diverse fragment binding data across isozymes within protein presents a unique opportunity to attack the selectivity problem in drug discovery. By comparing fragment in- teraction patterns across a large number of proteins, never feasible before, an assessment of differential bind- ing between proteins to modulate and proteins to leave unaffected is now more practical. As widely adopted, the service would provide a key tool to reduce toxicities due to off-target interactions, resulting in improved success rates of clinical trials.

抽象的意义：这是为药物研究人员提供引人入胜的新工具的目的在毒品的理性设计中，我们的选择性脱颖而出。化学片段的地图与1,000蛋白（玻尔兹曼地图）紧密结合，以寻找不同在蛋白质家族的碎片的空间结合模式中。攻击脱靶毒性挑战，困扰着药物发现中的念珠菌衍生物。我们的团队目前得到了Ann NIH II期SBIR Grant（2R44GM109549）的支持基于碎片的药物设计的大规模BMAP（1,000张蛋白质上的碎片地图）（请参阅） www.boltzmannmaps.com。搜索这些碎片图的磅比比较了大量蛋白质的结合模式。将可掌性掌握在整个行业中的药剂师手中显着改善了受脱靶毒性影响并加快交付的临床结果负面影响为患者提供新药物。创新：比较蛋白质中大量不同同工酶的化学片段结合结合基于我们独特的碎片绑定图存储库的家庭是对正确口粮的一种新科学方法选择性的设计。几何搜索算法。 AIM 1：开发一种新型算法，用于使用几何哈希，柔软的片段结合模式进行比较用其他参数（例如化学势排名的相对自由能和其他参数）本地绑定站点的生理化学特性。可视化搜索结果。目标2：验证AIM 1在概念验证项目中开发的工具，以评估所有权的选择性在蛋白质的Sirtuin家族中，有127种化合物确认实验数据。总体影响：总而言之，在蛋白质中具有跨同工酶的各种碎片结合数据提出了一个独特的机会，以攻击指南针片段中的选择性问题。跨大量蛋白质的特性模式，以前从不可行，对差异结合的评估现在，蛋白质调节蛋白质与蛋白质不受影响的蛋白质现在更加实用。服务w是什么主要是降低TOF目标相互作用的关键工具，并改善了临床试验的成功率。